Rational Number Compression Using Exponent-Difference Huffman Coding
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Solution Overview
Problem
Current data compression techniques, particularly for seismic data, face limitations in achieving high lossless compression ratios efficiently, leading to storage and transmission challenges due to the large size of seismic datasets and the need for preserving data precision.
Innovation Solution
A rational compression algorithm that represents numbers using their rational form, employing Huffman encoding and decoding based on the length and exponent differences of integer and floating-point numbers, allowing for efficient encoding and decoding without additional data structures, and utilizing multi-core computers for parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional compression algorithms are used on seismic data, then storage space is reduced, but compression ratio is limited and data precision may be lost
Solution Approach 1:
The patent changes the representation parameters of numerical data by decomposing floating-point numbers into exponent and significand components, then encoding these components separately using Huffman coding based on their statistical distributions. This parameter transformation enables higher compression ratios while preserving the exact numerical values, thus maintaining data precision.
Solution Approach 2:
The patent segments the floating-point number representation into distinct components (exponent and significand) and applies separate encoding strategies to each component. By analyzing and encoding the statistical distribution of each segment independently, the algorithm achieves better compression efficiency without losing any numerical information.
2Quantity of substance
If compression algorithms process large seismic datasets, then storage efficiency improves, but processing time increases
Solution Approach 1:
The patent performs preliminary statistical analysis of the exponent and significand distributions before compression, building Huffman coding tables in advance. This preliminary characterization of data patterns allows the main compression process to proceed efficiently by simply looking up pre-computed codes, rather than performing complex optimization during compression itself.
3Reliability
If lossless compression is applied to maintain data precision, then data integrity is preserved, but compression ratio is reduced compared to lossy methods
Solution Approach 1:
The patent transforms the numerical representation parameters into a form that is more amenable to compression while maintaining exactness. By representing floating-point numbers as pairs of integers (exponent and significand) and encoding their statistical distributions, the method achieves lossless compression ratios comparable to or better than lossy methods, while fully preserving data integrity.
Data Source
AI summary
Methods and apparatus are provided for rational compression of numbers. One or more numbers are compressed by obtaining a plurality of numbers (e.g., integer numbers and/or floating point numbers), wherein each of the plurality of numbers is represented using zero or more bits to indicate an exponent at a given base of the given number and has a length representing a number of significant bits of the given number; for each of the plurality of numbers, computing the length and a difference between the length and the exponent and counting occurrences of the length given the occurrence of the difference; generating a Huffman encoding comprising at least one Huffman tree based on the counting of the occurrences; generating one or more encoding maps comprising an array mapping each of the difference and the length to a corresponding Huffman prefix code; and encoding at least one of the plurality of numbers using the one or more encoding maps.